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Exploring a Trend-Following Strategy for Coffee Futures Trading
The Trend-Following strategy discussed in this article has demonstrated promising results for trading Coffee futures. Read on to learn more.
In this article, we analyze the creation of a systematic trading strategy for coffee futures (@KC), traded on the Intercontinental Exchange (ICE) in New York. Our aim is to enhance a trading portfolio by including lesser-known assets, in contrast to more frequently traded futures.
Characteristics of Coffee Futures
Coffee futures are categorized as “soft commodities,” which also encompass contracts for sugar, cocoa, cotton, and orange juice. This market offers several attractive features, such as significant intraday volatility, a low correlation with equity futures, and a solid trading volume.
Utilizing a Trend-Following approach, a strategy historically effective in various commodity markets, we seek to ascertain whether this methodology can be successfully applied to trading coffee futures.
Developing the Trend-Following Strategy
We are designing a Trend-Following strategy based on the breakout of significant highs and lows. Specifically, this system will enter a long position when the price exceeds the highest high recorded over a set number of sessions, expressed as Highest(High, n_sessions). Conversely, a short position will be initiated when the price dips below the lowest low over a designated number of sessions, known as Lowest(Low, n_sessions).
The system operates throughout the Coffee futures trading session, which spans from 4:15 AM to 1:30 PM (exchange time), Monday through Friday. All entries and exits occur on the same trading day, closing all positions by the end of the session unless a stop loss is triggered earlier. For this initial analysis, we have established a Stop Loss at $1,500.
We evaluate our trading system using a 15-minute timeframe (data1) for executing trades and a daily timeframe (data2) for determining entry levels, utilizing historical data from early 2010 to the conclusion of 2023.
This concept is succinctly coded as follows:
input:n_sessioni(5),mystop(1000),myprofit(0);
if EntriesToday(d)=0 then Buy next bar at Highest(High,n_sessioni)data2 stop;
if EntriesToday(d)=0 then Sellshort next bar at Lowest(Low,n_sessioni)data2 stop;
if mystop>0 then setstoploss(mystop);
if myprofit>0 then setprofittarget(myprofit);
setstopcontract;
setexitonclose;
The next step in enhancing the strategy involves optimizing the number of sessions (“n_sessions“) for calculating breakout levels. We will test values ranging from 1 to 10 in increments of 1 to assess their performance impact.
Figure 1 – Optimization of the “n_sessions” input to identify the optimal number of sessions for calculating highs and lows.
Our optimization indicates that increasing the number of sessions typically results in a decline in net profit and other performance metrics, although maximum drawdown remains relatively stable. Consequently, we select n_sessions = 2, meaning that our entry levels consider the past two sessions’ highs and lows. A breakout beyond these levels initiates a position.
The initial test results show a profitable and consistently growing equity curve, suggesting that Coffee futures respond well to Trend-Following strategies. Though further refinements are needed, these early results are encouraging, with an average trade generating $62 and a total net profit of $151,000. This marks a promising start for a preliminary system lacking additional filtering or optimizations.
Figure 2 – Detailed equity curve of the Trend Following strategy on Coffee futures.
Figure 3 – Strategy Performance Summary and Total Trade Analysis of the Trend Following strategy on Coffee futures.
Refining the Trading Window
To further optimize our Trend-Following trading system, we will analyze the impact of adjusting the operational time window—rather than executing the strategy for the full trading session.
We will modify the code to include a custom function with two new input parameters: “MyStartTrade” and “MyEndTrade“, which define the time window for allowed trades. Following this, we’ll optimize these parameters.
var:istartofsession(false);
istartofsession=_OHLCMulti5(0415,1330,ohlcvalues);
array:ohlcValues[23](0);
input: stoploss(1500), takeprofit(0);
input: MyStartTrade(415),MyEndTrade(1330);
input: n_sessioni(2);
//ingressi
if tw(MyStartTrade,MyEndTrade)
and EntriesToday(d)=0
then begin
Buy next bar at Highest(High,n_sessioni)data2 stop;
Sellshort next bar at lowest(Low,n_sessioni)data2 stop;
end;
if stoploss>0 then setstoploss(stoploss);
if takeprofit>0 then setprofittarget(takeprofit);
setstopcontract;
setexitonclose;
Figure 4 – Optimization of the “MyStartTrade” input to determine the best time to start trading.
Figure 5 – Optimization of the “MyEndTrade” input to determine the best time to stop trading.
Optimization findings indicate that reducing the trading window results in better overall performance. For instance, changing the start time to 5:00 AM and the end time to 12:00 PM significantly lowers the maximum drawdown from over $25,000 to less than $20,000.
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Refining Coffee Futures Trading: Price Patterns and Day Exclusions
Early trading movements often display erratic behavior and lack a clear direction, making it challenging to open new trades. Consequently, initiating trades later in the day is not practical, as all positions must be closed by the end of the trading day.
Improving Strategy: Adding a Price Pattern Filter
To enhance our Trend-Following strategy, we will focus on a critical metric: average trade value. For this strategy to succeed in live trading, the average trade value must be sufficiently high to cover costs like commissions and slippage. This is particularly important for trading coffee futures, where each tick is valued at $18.75.
One potential enhancement involves incorporating a filter based on the “Daily Factor” (DF). This pattern measures the price movement from open to close relative to the total daily range (high to low).
Notably, the Daily Factor does not indicate market direction; it only reflects volatility. A low Daily Factor indicates minimal price movement within the total range, while a high Daily Factor suggests strong price movement, pointing to a more bullish or bearish trading session.
To determine when to trade, we calculate the Daily Factor (“DF“) and compare it to a predefined threshold (“DF_level“). Both “DF” and “DF_level” values range from 0 to 1.
We will implement this filter by adding the following lines to our trading script and optimize the “DF_level” parameter. We’ll test values between 0.1 and 1 in increments of 0.05.
//Daily Factor
input:DF_level(1);
var: DF(0);
if (highs(1) – lows(1))<>0 then begin
DF=absvalue(opens(1) – closes(1)) / (highs(1) – lows(1));
end;
Figure 6 – Optimization of the “DF_level” input used to define the filter based on the Daily Factor pattern.
After optimizing, we select a value of 0.8. This means that the strategy will only open a position if the previous session’s price movement (Open-Close) is less than 80% of the total range (High-Low). This adjustment boosts our average trade by $20 to over $80, while also reducing maximum drawdown and enhancing net profit.
Further Strategy Enhancements: Excluding Certain Weekdays
While our testing shows notable improvements, the system still requires further refinement before moving to live trading. One approach is to analyze historical data to exclude specific days of the week that have consistently underperformed.
We update our script with two new inputs, “skipdaylong” and “skipdayshort,” to specify the trading days to bypass. We then optimize these inputs within the range of 1 to 6, aligning with the MultiCharts programming language, where these numbers represent days of the week—1 for Monday and 6 for Saturday.
input: skipdaylong(-1), skipdayshort(-1);
if dayofweek(d) <> skipdaylong then Buy next bar at Highest(High,len)data2 stop;
if dayofweek(d) <> skipdayshort then Sellshort next bar at Lowest(Low,len)data2 stop;
Figure 7 – Optimization results for the “skipdaylong” and “skipdayshort” inputs, organized by average trade.
To validate our optimization findings, we employ a proprietary program that analyzes the bias of each trading instrument at various levels—from intraday, hourly to monthly and yearly analyses. Reviewing Figure 8, we observe that on Mondays (highlighted in the figure), Coffee futures tend to lack a defined trend. Conversely, other weekdays demonstrate clearer trends, often bearish for this commodity. Additionally, Fridays exhibit a weaker Daily Factor than other days. Therefore, we will exclude Mondays from our trading, moving towards a higher average trade goal of $100.
Figure 8 – Weekly bias outcomes for Coffee futures, analyzed using proprietary Bias Finder software.
Final Strategy Adjustments: Exit Optimization
The last step in our testing involves optimizing the stop-loss, previously set at $1,500. We also introduce a profit target to evaluate potential benefits from this addition. In this final phase, we establish both the stop-loss and profit target at $1,700.
Our final analysis yields a net profit of $167,000, with an average trade value of $107 and a maximum drawdown of approximately $13,000.
Figure 9 – Stop Loss value optimization for the Trend Following strategy focusing on Coffee futures.
Figure 10 – Profit Target value optimization for the Trend Following strategy focusing on Coffee futures.
Figure 11 – Final equity curve for the strategy on Coffee futures.
Figure 12 – Final Performance Summary and Total Trade Analysis for the strategy on Coffee futures.
Conclusions on the Trend Following Trading System for Coffee Futures
In summary, the Trend Following approach described has demonstrated considerable potential for trading Coffee futures. This highlights the benefits of exploring lesser-known markets to achieve broad portfolio diversification. While our analysis offers valuable insights, further refinements are necessary before transitioning to live trading. Focus will remain on identifying the most profitable trades and ensuring that the average trade is robust for real-world conditions.
Until next time, happy trading!
Andrea Unger
